import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain_output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field

class TaskCard(BaseModel):
    title: str = Field(..., description="Title of the task")
    subject: str = Field(..., description="Subject area")
    deadline_hint: str | None = Field(None, description="Hint about deadline")
    deliverable_type: str = Field(..., description="What to submit (report, code, etc.)")
    grading_hints: list[str] = Field(default_factory=list, description="Hints for grading")

llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)
parser = PydanticOutputParser(pydantic_object=TaskCard)
prompt_template = """
You are a task summarizer.
Given the following informal description of an assignment, produce a JSON object matching TaskCard model.

Description: {description}

{format_instructions}
"""
prompt = prompt_template | llm | parser
async def main():
    description = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
    result = await prompt.ainvoke({"description": description})
    print(result["messages"][-1].content)

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())